T. Bera and L. Higgins. ARCH Models: Properties, Estimation and Testing. Journal of Economic Surveys, 7(4), 1993. Home/Search Document Not in Database Summary Related Articles Check |
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Pareto Evolutionary Neural Networks - Fieldsend, Singh (2003) (Correct)
.... is opposed to the rigid structural form of most econometric time series forecasting methods, e.g. auto regressive (AR) models, exponential smoothing models, generalised) auto regressive conditional heteroskedasticity models ( G)ARCH) and auto regressive integrated moving average models (ARIMA) [6, 22]. Apart from this important difference, the underlying approach to time series forecasting itself has remained relatively unchanged during its progression from explicit regression modelling to the non linear generalisation approach of NNs. Both of these approaches are typically based on the ....
T. Bera and L. Higgins. ARCH Models: Properties, Estimation and Testing. Journal of Economic Surveys, 7(4), 1993.
Trading Volume in Models of Financial Derivatives - Howison, Lamper (2000) (Correct)
.... popular approaches include: hyperbolic distribution [16] Student s t distribution [6] L evy stable non Gaussian model [43, 51] truncated L evy ight [44] multi fractal processes [42] The discrete time modelling of volatility has also been particularly popular in the econometrics literature [4, 8]. Volatility is not found to be constant, but to vary over time and exhibit positive serial correlation, i.e. volatility clustering. The most successful models have been the family of autoregressive conditional heteroskedastic (ARCH) models [17] and its extension into GARCH [7] and more recently ....
Anil K. Bera and Matthew L. Higgins, ARCH models: Properties, estimation and testing, Journal of Economic Surveys 7 (1993), no. 4, 305-366.
Neural Networks for Time Series Processing - Dorffner (1996) (11 citations) (Correct)
....which (e.g. mean and standard deviation) can be included in the modeling process. By doing this in forecasting, for instance, one cannot only give an estimate of the forecast value, but also an estimate of how much this value will be disturbed by noise. This is the focus of so called ARCH models [4]. 2.4 Preprocessing of time series In only a few cases it will be appropriate to use the measured observables immediatly for processing. In most cases, it is necessary to pre analyze, as well as preprocess the time series to ensure an optimal outcome of the processing. One one hand, this has to ....
Bera A.K., Higgins M.L.: ARCH models: properties, estimation and testing, Journal of Economic Surveys 7(4), 307-366, 1993.
Pareto Multi-Objective Non-Linear Regression Modelling to .. - Jonathan Fieldsend And (2002) (Correct)
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T. Bera and L. Higgins. ARCH Models: Properties, Estimation and Testing. Journal of Economic Surveys, 7(4), 1993.
Pareto Multi-Objective Non-Linear Regression Modelling to .. - Jonathan Fieldsend And (2002) (Correct)
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T. Bera and L. Higgins. ARCH Models: Properties, Estimation and Testing. Journal of Economic Surveys, 7(4), 1993.
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